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lasist distributes ACG(c) grouper (designed by the John Hopkins University), the population based case-mix system used at international level for financing and managing patients in Primary Care and capitation areas, exclusively in Spain and Portugal.
An innovative perspective
The Adjusted Clinical Groups (ACG) patient classification system offeres an innovative perspective in health measurement in a population, being based on comorbidity levels.
The ACGs consist of risk adjustment methodology which can be used to evaluate the operation of providers more precisely and equitably, to anticipate the use of health services and establish more equitable payments and budgets.
Who is it aimed at?
The ACG system creates a common language for the analysis of health services that can benefit service providers, buyers and consumers. The ACG system is used in a wide range of applications that require risk adjustment:
- To establish capitation payments for provider groups
- To evaluate the efficiency of providers
- To predict high risk users for their clinical management
- To distribute resources more fairly in the health areas
- To evaluate accessibility to services
- To improve quality
A friendly tool Kit: much more than a grouper
The Johns Hopkins University ACG system is more than just a simple risk adjustment model: it is a family of tools designed to help explain how health resources are distributed and used.
ACG Grouper
The basis of the system is the Adjusted Clinical Groups (ACG) algorithm. Each ACG classifies people into single, mutually exclusive, morbidity categories based on disease patterns and on the expected use of resources. ACGs can be used more successfully instead of traditional categories of age/sex to try to explain the variations in the morbidity rate between two or more patient populations.
Expanded Diagnostic Clusters (EDC)
To give a greater clinical significance to the case-based reasoning, the ACG software system also includes Expanded Diagnosis Clusters (EDCs). EDCs are diagnostic code groups that describe identical or related conditions, being useful for examining the epidemiology of the diseases in a population. They can also be used as disease or condition markers in different applications, such as identifying patients who could be included in specific managed medicine programmes.
Predictive model
The ACG system includes a sophisticated predictive model (ACG-PM(r)) which has been calibrated to identify patients with an increased risk of using a large amount of health resources in the future and to calculate its potential cost. Before the patient's state of health gets worse and consumes numerous health resources, the ACG-PM(r) helps to identify people who might benefit from a more intense management of their process (disease management, case management, etc.). The ACG-PM(r) is also useful for predicting future resource consumption of determined population groups and has many applications in the field of quality improvement.
Minimum information requirements
ACGs are obtained exclusively with diagnostic coding data recorded in medical reports, or collected in the electronic clinical histories, the age and sex of patients Data from surveys or the like are not needed.
One system Many tools Many applications Many benefits
ACGs are available under the auspices of a world renowned academic institution: the Johns Hopkins University This university has been investigating risk measurement and its categorisation for over 25 years. The Johns Hopkins University Bloomberg School of Public Health is firmly committed to the continued development of ACGs and their dissemination among public, as well as private sector users.
